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Induction of CB2 receptor expression in the rat spinal cord of neuropathic but not inflammatory chronic pain models

2003· article· en· W2045531190 on OpenAlexaff
Ji Zhang, Cyrla Hoffert, Huy Khang Vu, Thierry Groblewski, Sultan Ahmad, Dajan O’Donnell

Bibliographic record

VenueEuropean Journal of Neuroscience · 2003
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsAstraZeneca (Canada)
Fundersnot available
KeywordsNeuropathic painChronic painMedicineSpinal cordCannabinoid receptor type 2MicrogliaReceptorNociceptionInflammationAnalgesicPharmacologyNeuroscienceImmune systemCannabinoid receptorImmunologyBiologyInternal medicineAgonistPhysical therapy

Abstract

fetched live from OpenAlex

Cannabinoids have been considered for some time as potent therapeutic agents in chronic pain management. Central and systemic administration of natural, synthetic and endogenous cannabinoids produce antinociceptive and antihyperalgesic effects in both acute and chronic animal pain models. Although much of the existing data suggest that the analgesic effects of cannabinoids are mediated via neuronal CB1 receptors, there is increasing evidence to support a role for peripheral CB2 receptors, which are expressed preferentially on immune cells. As yet, little is known about the central contribution of CB2 in neuropathic pain states. We report here that chronic pain models associated with peripheral nerve injury, but not peripheral inflammation, induce CB2 receptor expression in a highly restricted and specific manner within the lumbar spinal cord. Moreover, the appearance of CB2 expression coincides with the appearance of activated microglia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.299
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations414
Published2003
Admission routes1
Has abstractyes

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